EnvX：以代理式AI實現萬物代理化

摘要

開源程式庫的廣泛可用性已催生了大量可重用的軟體元件，然而它們的使用仍依賴手動操作，容易出錯且缺乏連貫性。開發者必須查閱文件、理解API並編寫整合程式碼，這為高效的軟體重用設置了重大障礙。為解決這一問題，我們提出了EnvX框架，該框架利用代理型人工智慧（Agentic AI）將GitHub程式庫轉化為智慧型、自主的代理，使其能夠進行自然語言互動和代理間協作。與將程式庫視為靜態程式碼資源的現有方法不同，EnvX通過三個階段重新構想它們為活躍的代理：(1) 基於TODO的環境初始化，設置必要的依賴項、資料和驗證資料集；(2) 與人類目標對齊的代理自動化，使特定於程式庫的代理能夠自主執行實際任務；(3) 代理間（A2A）協議，允許多個代理進行協作。通過將大型語言模型的能力與結構化工具整合相結合，EnvX不僅自動化程式碼生成，還自動化了理解、初始化和操作化程式庫功能的整個過程。我們在GitTaskBench基準上評估了EnvX，使用了涵蓋影像處理、語音識別、文件分析和影片操作等領域的18個程式庫。結果顯示，EnvX達到了74.07%的執行完成率和51.85%的任務通過率，優於現有框架。案例研究進一步展示了EnvX通過A2A協議實現多程式庫協作的能力。這項工作標誌著從將程式庫視為被動程式碼資源到智慧型、互動式代理的轉變，促進了開源生態系統中更大的可訪問性和協作性。

English

The widespread availability of open-source repositories has led to a vast collection of reusable software components, yet their utilization remains manual, error-prone, and disconnected. Developers must navigate documentation, understand APIs, and write integration code, creating significant barriers to efficient software reuse. To address this, we present EnvX, a framework that leverages Agentic AI to agentize GitHub repositories, transforming them into intelligent, autonomous agents capable of natural language interaction and inter-agent collaboration. Unlike existing approaches that treat repositories as static code resources, EnvX reimagines them as active agents through a three-phase process: (1) TODO-guided environment initialization, which sets up the necessary dependencies, data, and validation datasets; (2) human-aligned agentic automation, allowing repository-specific agents to autonomously perform real-world tasks; and (3) Agent-to-Agent (A2A) protocol, enabling multiple agents to collaborate. By combining large language model capabilities with structured tool integration, EnvX automates not just code generation, but the entire process of understanding, initializing, and operationalizing repository functionality. We evaluate EnvX on the GitTaskBench benchmark, using 18 repositories across domains such as image processing, speech recognition, document analysis, and video manipulation. Our results show that EnvX achieves a 74.07% execution completion rate and 51.85% task pass rate, outperforming existing frameworks. Case studies further demonstrate EnvX's ability to enable multi-repository collaboration via the A2A protocol. This work marks a shift from treating repositories as passive code resources to intelligent, interactive agents, fostering greater accessibility and collaboration within the open-source ecosystem.

EnvX：以代理式AI實現萬物代理化

EnvX: Agentize Everything with Agentic AI

摘要

Support